Prominent feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a computational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique that relies on statistical classiﬁcation to compute signatures has the potential to be much faster but at the cost of using very large amounts of memory, which makes it impractical for implementation

on low-memory devices. In this paper, we show that we can exploit the sparseness of these signatures to compact them, speed up the computation, and drastically reduce memory usage. We base our approach on Compressive Sensing theory. We also highlight its effectiveness by incorporating it into two very different SLAM packages and demonstrating substantial performance increases.

"...Remarkably, as will be shown in the experimental section, using either a Random Ortho-Projection or a PCA projection yields virtually the same results. This sheds light on the inner workings of the many methods [16], which perform PCA dimensionality reduction of SIFT-style descriptors. It suggests that their success owes more to the underlying sparsity of these descriptors than to PCA itself...."